In an effort to capture semantic information in the image, Boominathan et al. [1]} introduced CrowdNet, a deep learning-based framework for estimating crowd density from static images of highly dense crowds. The authors exploited a combination of deep and shallow fully convolutional networks to predict the density map for a given crowd image. The deep network captures the high-level semantic information for objects near the camera captured in a significant level of detail (e.g., faces/body detectors). In contrast, the shallow one captures basic low-level patterns for objects away from the camera or when images are captured from an aerial viewpoint (e.g., representing as a head blob a person). Like in the MCNN architecture, in the end, the features maps produced by the two branches are merged, and a final \(1\times 1\)  convolutional layer is responsible for predicting the final density map. The overview of the architecture is shown in fig:overviewcrowdnet.
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